arXiv:2312.15341v1 Announce Type: cross Abstract: We provide an overview of recent progress in statistical inverse problems with random experimental design, covering both linear and nonlinear inverse problems. Different regularization schemes have been studied to produce robust a…
arXiv stat.ML
TIER_1English(EN)·Andrea Nava, Peter B\"uhlmann, Fabio Sigrist·
arXiv:2607.09371v1 Announce Type: new Abstract: Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfounding mitigates this problem by shrinking high-vari…
Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfounding mitigates this problem by shrinking high-variance directions of the covariate matrix that, un…
arXiv:2511.15060v2 Announce Type: replace-cross Abstract: Total variation (TV) regularization is a classical edge-preserving technique widely used across image recovery and reconstruction problems; however, its convex $\ell_1$ gradient penalty tends to over-shrink large gradients…
arXiv stat.ML
TIER_1English(EN)·Abhishake Rastogi, Tatiana A. Bubba, Tapio Helin, Luca Ratti·
arXiv:2607.07468v1 Announce Type: new Abstract: We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning. The unknown is modeled as an element of $\ell^1$, and observations are generated through a pos…
We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning. The unknown is modeled as an element of $\ell^1$, and observations are generated through a possibly nonlinear forward operator $A:\ell^1\to H$…
